Template-based analysis and classification of cardiovascular waveforms

ABSTRACT

In various embodiments, a first classification assigned to a periodic component of an electrical waveform that represents electrical activity in a patient&#39;s heart may be identified ( 302 ). A corresponding periodic component of a hemodynamic waveform that represents hemodynamic activity in the patient&#39;s cardiovascular system may be analyzed ( 306, 318, 328 ). The corresponding periodic component may be causally related to the periodic component of the electrical waveform. Based on the analysis, the previously-assigned classification may be assigned ( 312, 324 ) to the corresponding periodic component of the hemodynamic waveform in response to a determination, based on the analyzing, that the previously-assigned classification also applies to the corresponding periodic component. In a database ( 130 ) of hemodynamic templates, a hemodynamic template associated with the previously-assigned classification may be updated ( 314 ) to include one or more features of the corresponding periodic component of the hemodynamic waveform.

TECHNICAL FIELD

The present disclosure is directed generally to health care. Moreparticularly, but not exclusively, various methods and apparatusdisclosed herein relate to template-based analysis and classification ofcardiovascular waveforms.

BACKGROUND

Cardiovascular waveforms representing arterial blood pressure (“ABP”),pulmonary artery pressure (“PAP”), central venous pressure (“CVP”), andplethysmography may be impacted by physiological changes such as ectopicbeats and/or arrhythmias. In particular, electrical activity in apatient's heart, e.g., as measured by an electrocardiogram (“ECG”), mayinfluence the shapes of these waveforms. A physician may then analyzethe shape of these waveforms to identify abnormalities in the patient'sheartbeat that warrants further investigation. However, the signalobtained by ECG and other devices is not perfect, noise and artifactscan be introduced which also affect the waveform shape. These shapes cansometimes be mistaken for abnormalities, even though they wereintroduced by the machine or other factor other than the patient'sphysiology.

SUMMARY

Algorithms for filtering and evaluating waveform quality sometimesincorrectly classify atypical electrical activity as noise or artifacts(e.g., due to interference and/or patient movement) when in fact theactivity may evidence cardiovascular abnormalities. For example, somealgorithms compare electrical and/or hemodynamic waveforms to templatesof normal and abnormal waveforms. However, many abnormal waveforms maynot match existing templates and may consequently lead to a noisyclassification, when in fact a true cardiovascular abnormality exists.Thus, it would be beneficial to provide a method and system to analyzeand classify cardiovascular waveforms in a manner that better identifiescardiovascular abnormalities and corroborates/refutes classificationsmade using existing algorithms.

The present disclosure is directed to inventive methods and apparatusfor template-based analysis and classification of cardiovascularwaveforms. For example, the present disclosure describes techniques forclassifying (or annotating) periodic components of hemodynamicwaveforms, such as heart beats, based on various signals such astemplates of normal and abnormal waveforms, and/or for corroboratingand/or reclassifying periodic components of electrical waveforms thatrepresent electrical activity in patients' hearts. Moreover, templatesassociated with normal waveforms and various types of abnormal waveformsmay be updated to include features of newly-identified normal andabnormal waveforms.

Generally, in one aspect, a method may include identifying apreviously-assigned classification associated with a periodic componentof an electrical waveform, wherein the electrical waveform representselectrical activity in a patient's heart; analyzing a correspondingperiodic component of a hemodynamic waveform that represents hemodynamicactivity in the patient's cardiovascular system, wherein thecorresponding periodic component is causally related to the periodiccomponent of the electrical waveform; assigning the previously-assignedclassification to the corresponding periodic component of thehemodynamic waveform in response to a determination, based on theanalyzing, that the previously-assigned classification also applies tothe corresponding periodic component; and updating, in a database ofhemodynamic templates, a hemodynamic template associated with thepreviously-assigned classification to include one or more features ofthe corresponding periodic component of the hemodynamic waveform.

In various embodiments, the method may further include: receivingelectrophysiological data associated with the patient, wherein theelectrophysiological data includes the electrical waveform and one ormore previously-assigned classifications associated with one or moreperiodic components of the electrical waveform; and receivinghemodynamic data associated with the patient, wherein the hemodynamicdata includes the hemodynamic waveform.

In various embodiments, the electrophysiological data is received fromone or more electrodes of an electrocardiogram. In various embodiments,the hemodynamic data may include a signal indicative of arterial bloodpressure of the patient. In various embodiments, the hemodynamic datamay include a signal indicative of pulmonary blood pressure of thepatient. In various embodiments, the hemodynamic data may include asignal indicative of central venous pressure. In various embodiments,the hemodynamic data may include a signal from a plethysmograph.

In various embodiments, the method may further include: identifying anunclassified periodic component of the same hemodynamic waveform or adifferent hemodynamic waveform associated with a different patient;matching the unclassified periodic component to a template of thedatabase of hemodynamic templates; and assigning a classificationassociated with the matching template to the unclassified periodiccomponent of the hemodynamic waveform. In various versions, the methodmay further include updating the matching template to include one ormore features of the now-classified periodic component of thehemodynamic waveform.

In various embodiments, the previously-assigned classification mayinclude an abnormal classification. The assigning may include assigningthe abnormal classification to the corresponding periodic component ofthe hemodynamic waveform in response to a determination, based on theanalysis, that a delta between the corresponding periodic component ofthe hemodynamic waveform and a previous periodic component of thehemodynamic waveform satisfies a threshold.

In various embodiments, the method may further include: identifying anartifact classification assigned to another periodic component of theelectrical waveform deemed an artifact; analyzing another correspondingperiodic component of the hemodynamic waveform that is causally relatedto the another periodic component of the electrical waveform; assigningan abnormal classification to the another corresponding periodiccomponent of the hemodynamic waveform in response to a determination,based on the analyzing, that a delta between the another correspondingperiodic component of the hemodynamic waveform and another previousperiodic component of the hemodynamic waveform satisfies a threshold;and reclassifying the another periodic component of the electricalwaveform with the abnormal classification.

In various embodiments, the previously-assigned classification mayinclude a normal classification, and the assigning may include assigningthe normal classification to the corresponding periodic component of thehemodynamic waveform in response to a determination, based on theanalyzing, that the corresponding periodic component satisfies a signalquality index (“SQI”).

In various implementations, the analyzing may include matching thecorresponding periodic component to a template of the database ofhemodynamic templates. In various versions, the updating may includecoalescing the corresponding periodic component with a periodiccomponent stored in association with the matching hemodynamic template.

It should be appreciated that all combinations of the foregoing conceptsand additional concepts discussed in greater detail below (provided suchconcepts are not mutually inconsistent) are contemplated as being partof the subject matter disclosed herein. In particular, all combinationsof claimed subject matter appearing at the end of this disclosure arecontemplated as being part of the subject matter disclosed herein. Itshould also be appreciated that terminology explicitly employed hereinthat also may appear in any disclosure incorporated by reference shouldbe accorded a meaning most consistent with the particular conceptsdisclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, like reference characters generally refer to the sameparts throughout the different views. Also, the drawings are notnecessarily to scale, emphasis instead generally being placed uponillustrating the principles of the disclosure.

FIG. 1 illustrates an example environment in which disclosed techniquesmay be implemented, in accordance with various implementations.

FIG. 2 depicts example waveforms that may be analyzed in accordance withvarious embodiments.

FIG. 3 and FIG. 4 depict example methods in accordance with variousembodiments.

FIG. 5 depicts example “normal” and “abnormal” templates, in accordancewith various embodiments.

FIG. 6 depicts components of an example computer system.

DETAILED DESCRIPTION

Cardiovascular waveforms such as those representing ABP, PAP, and/orCVP, as well as a signal from a plethysmograph (“PLETH”) may be impactedby physiological changes such as ectopic beats and/or arrhythmias. Inparticular, electrical activity in a patient's heart, e.g., as measuredby an ECG, may influence the shapes of various hemodynamic waveformsExisting algorithms for filtering and evaluating waveform qualitysometimes incorrectly classify atypical electrical activity as noise orartifacts when in fact the activity may evidence cardiovascularabnormalities. For example, some algorithms compare electrical and/orhemodynamic waveforms to templates of normal and abnormal waveforms.However, many abnormal waveforms may not match existing templates andmay consequently lead to a noisy classification, when in fact a truecardiovascular abnormality exists. Thus, there is a need in the art toanalyze and classify cardiovascular waveforms in a manner that betteridentifies cardiovascular abnormalities and corroborates/refutesclassifications made using existing algorithms. More generally, it isrecognized and appreciated that it would be beneficial to continuouslylearn new patterns associated with normal and abnormal cardiovascularwaveforms. In view of the foregoing, various embodiments andimplementations of the present disclosure are directed to classifying orannotating periodic components of cardiovascular waveforms, such asheart beats, based on various signals such as templates of normal andabnormal waveforms, and/or for corroborating and/or reclassifyingperiodic components of electrical waveforms that represent electricalactivity in patients' hearts. Moreover, databases of templates of normaland abnormal waveforms may be updated to include features ofnewly-identified normal and abnormal waveforms.

Referring to FIG. 1, an example environment 100 is depicted in whichdisclosed techniques may be implemented. A patient 102 may be connectedto various medical devices to monitor electrical activity in thepatient's heart and/or hemodynamic activity in the patient's vascularsystem. For example, patient 102 may be monitored by one or more medicaldevices such as an ECG 104, an APB 106, a PAP 108, a CVP 110, and/or aPLETH 112. These devices are just examples and are not meant to belimiting. Other electrical and/or hemodynamic signals may be obtainedand monitored.

In the case of the ECG 104, in some implementations, one or moreelectrodes (not depicted) may be affixed to patient 102 to detectelectrical activity in the patient's heart to produce a signal 114. Inthe case of ABP 106, an instrument such as a sphygmomanometer may beused to measure ABP of patient 102, e.g., at regular intervals orcontinuously, to produce a signal 116. PAP 108 may take various forms,such a trans-thoracic echocardiogram (“TTE”) and/or right heartcatheterization, and may produce signal 118. In various embodiments, CVP110 may be measured by connecting a central venous catheter (notdepicted) to an infusion pump to produce signal 120. In the case ofPLETH 112, a signal 122 may be produced by a plethysmograph, which is aninstrument for measuring volume changes within an organ or whole body ofpatient 102.

In various embodiments, signals 114-122 may be provided to acardiovascular analysis system 124. The operations performed bycardiovascular analysis system 124 may be distributed across multiplecomputer systems. For instance, cardiovascular analysis system 124 maybe implemented as computer programs running on one or more computers inone or more locations that are coupled to each other through a network(not depicted). Cardiovascular analysis system 124 may include variousengines and/or modules that may be implemented using hardware, software,or any combination of the two. In various embodiments, these modulesand/or engines may include an initial annotation engine 125, analysisengine 126, and/or a template engine 128. In some implementations one ormore of engines 125, 126 and 128 may be omitted. In some implementationsall or aspects of one or more of engines 125, 126 and 128 may becombined. In some implementations, one or more of engines 125, 126 and128 may be implemented in a component that is separate fromcardiovascular analysis system 124.

Initial annotation engine 125 may be configured to analyze, inisolation, electrical signal 114 produced by ECG 104 for variouscharacteristics. In some embodiments, initial annotation engine 125 mayannotate various “periodic components” of an electrical waveform as“normal,” “abnormal,” or as an “artifact” (a.k.a. “noise”). As usedherein, a “periodic component” of a waveform may refer to any componentthat typically occurs repeatedly in the waveform (though not necessarilyidentically from occurrence to occurrence). In some embodiments, theperiodic component may be a recurring “peak” of the waveform. Forexample, in an electrical waveform, each “peak” may represent a surge inelectrical activity. In other embodiments, the periodic waveform mayrefer to a “valley” or another typically recurring visual feature of thewaveform such as a peak/valley pair (even though height or phase of thepeak and depth of the valley may differ from occurrence to occurrence).The modifier “periodic” as used herein is meant to refer to thecomponent being something that normally occurs periodically, or at thevery least is supposed to occur periodically. It should be understood,however, that in various scenarios, particularly with severely ailingpatients, a waveform produced by the patient may or may not exhibit theperiodic components, and/or the periodic components may not actuallyoccur periodically with a particular patient (this may or may not be acause or result of the patient's ailment). In some embodiments, theannotations may be added by initial annotation engine 125 using variousalgorithms such as the Segment and Arrhythmia Analysis ST/AR ECG and/orDXL algorithms, although annotations may be added using other algorithmsas well.

In various implementations, analysis engine 126 may be configured toanalyze annotated waveforms representing electrical activity in apatient's heart and/or hemodynamic activity in a patient's vascularsystem to determine whether one or more annotations, or“classifications,” are correct, incorrect, etc. In a hemodynamicwaveform, each “peak” (or combination of peak and valley) may representa heartbeat. In various implementations, analysis engine 126 may beconfigured to identify a classification/annotation assigned, e.g., byinitial annotation engine 125, to a periodic component of an electricalwaveform that represents electrical activity in a heart of patient 102.For example, analysis engine 126 may identify a particular peak that isclassified or annotated as “normal,” “abnormal,” or as an “artifact.”Analysis engine 126 may then analyze a corresponding periodic componentof a hemodynamic waveform that represents hemodynamic activity in thepatient's vascular system (e.g., representing one or more of signals116-122). The corresponding periodic component may be causally relatedto the periodic component of the electrical waveform.

Based on the analysis, analysis engine 126 may classify (or reclassifyif already classified elsewhere) the corresponding periodic component ofthe hemodynamic waveform with the same classification as was assigned tothe periodic component of the electrical waveform or a differentclassification. For example, if analysis of the hemodynamic waveformcorroborates a classification of “normal” assigned to a periodiccomponent of the electrical waveform by initial annotation engine 125,then analysis engine 126 may classify the corresponding periodiccomponent of the hemodynamic waveform as “normal.” On the other hand,suppose the analysis contradicts an “artifact” classification assignedto the periodic component of the electrical waveform by initialannotation engine 125. For example, suppose analysis engine 126determines using techniques described herein that the correspondingperiodic component of the hemodynamic waveform actually indicates anabnormality. In such case, analysis engine 126 may classify thecorresponding periodic component of the hemodynamic waveform as“abnormal,” and may reclassify the periodic component of the electricalwaveform from “artifact” to “abnormal.”

In various implementations, once analysis engine 126 classifies and/orreclassifies a periodic component of an electrical and/or hemodynamicwaveform, analysis engine 126 may effectively “update” its knowledgeand/or the knowledge of cardiovascular analysis system 124. For example,in some embodiments, a template engine 128 may maintain one or moredatabases of hemodynamic templates. In various embodiments, eachhemodynamic template may include one or more “features” of one or moreperiodic components of one or more hemodynamic waveforms with the sameclassification as the periodic component of the hemodynamic waveform. InFIG. 1, template engine 128 maintains a database 130 of hemodynamictemplates that includes templates associated with a “normal”classification, as well as templates have various types of “abnormal”classifications associated with different ailments (e.g., ectopic beats,arrhythmias, etc.). However, this is not meant to be limiting. In otherembodiments, templates classified as normal, abnormal, or even“artifacts,” may be stored in separate databases.

After analysis engine 126 performs the analysis described above, invarious embodiments it may request that template engine 128 update oneor more of template databases 130 with one or more features of thecorresponding periodic component of the hemodynamic waveform. Variousfeatures of periodic components of waveforms or the underlyinghemodynamic activity may be used to update templates, ranging fromcomprehensive data representing the entire waveform, to specificfeatures such as the maximum/minimum amplitude, distance from a priorpeak, rate of increase/decrease, turbulence, blood density, meanvelocity, blood viscosity, and so forth. In some embodiments, featuresof multiple waveforms (e.g., electrical and hemodynamic) may be added toa template. For example, “pulse transit time” may be a time differencebetween a peak of an ECG waveform and a valley of an ABP or PLETHwaveform. In some embodiments, a pulse transit time may be included in atemplate as an annotation to a periodic component.

When new or otherwise unclassified hemodynamic waveforms are beinganalyzed by analysis engine 126, it may compare periodic components ofthe new hemodynamic waveforms with the templates in database 130. Atemplate may match a particular periodic component under analysis when,for instance, one or more features of the periodic component underanalysis are sufficiently similar to corresponding features of thetemplate.

For example, in some embodiments, a feature vector may be extracted fromthe periodic component under analysis. Various machine learningtechniques may be employed to calculate similarity measures between atemplate feature vector associated with the template and the featurevector of the periodic component under analysis. In some embodiments,mathematical models such as neural networks and/or logistical regressionmay be trained. Various learning algorithms for training such models maybe used such as, for example, batch or stochastic gradient descentand/or application of the normal equations. If the calculatedsimilarities satisfy one or more thresholds, there may be a match.Consequently, the periodic component under analysis may be classifiedthe same as the template feature vector. In some implementations, one ormore features from the feature vector extracted from the periodiccomponent may be incorporated into a template of template database 130,e.g., to aid in future comparisons.

In other embodiments, a hemodynamic template may include one or moreperiodic components that, for instance, represent a coalescing of aplurality of previously-classified periodic components. The periodiccomponent under analysis may be correlated to the one or more periodiccomponents of the template to determine whether there is a match (e.g.,if a difference between the two satisfies or fails to satisfy one ormore thresholds). In some embodiments, the periodic component underanalysis may be subtracted from the template periodic component(s), andthe difference may be compared to the one or more thresholds. In otherembodiments, techniques such as Fast Fourier Transforms (“FFT”) orcovariance shifting may be employed to correlate the periodic componentunder analysis to the template periodic component.

Referring now to FIG. 2, two example waveforms are depicted. A firstwaveform 240 depicted in solid line represents electrical activity of apatient, e.g., the signal 114 produced by ECG 104. A second waveform 242depicted in dashed line represents hemodynamic activity in the patient'svascular system, e.g., the signal 116 produced by ABP 106. In thisexample, five periodic components (left to right) of first waveform 240in the form of peaks are annotated/classified with the letter “N” toindicate normal electrical activity in the patient's heart, e.g., asdetermined by initial annotation engine 125. The sixth electrical peakis annotated with the letter “A” to indicate an “artifact” perceived byinitial annotation engine 125, e.g., due to a sudden increase inamplitude and/or abrupt decrease in phase. Following the artifact, thereare four more periodic components of first waveform classified asnormal.

Periodic components of second waveform 242, which in this example takethe form of peaks, are causally related to periodic components of firstwaveform 240. In particular, an electrical pulse (as represented by apeak) in first waveform 240 is followed by a consequent peak in secondwaveform 242. This is because the electrical pulse in the patient'sheart triggers the heart to pump blood, and the peak blood pressureoccurs some time interval after the peak electrical pulse. In thisexample, there are five similar peaks in second waveform 242 withrelatively normal and uniform amplitudes, followed by a higher peak,then a lower peak, followed by four more peaks that approach normal peakamplitude.

As indicated at the symbol Δ, there is a difference in ABP measuredbetween the sixth and seventh peaks of second waveform 242, whichappears to have been a consequence of the electrical periodic componentclassified as an “artifact.” The fact that the second waveform 242exhibits this A apparently as a result of the so-called “artifact” infirst waveform 240 suggests that the artifact is not an artifact at all.Rather, the corresponding abnormality in second waveform 242demonstrated by A suggests a drop in systolic ABP caused by a prematurebeat, which results in insufficient blood being pumped by the patient'sheart into the vascular system. In other words, a physiological abnormalelectrical pulse has caused a physiological abnormal hemodynamic pulse.Using techniques described herein, the periodic component of secondwaveform 242 indicated at Δ may be classified as “abnormal.” In someembodiments, the periodic component (i.e. electrical pulse) of firstwaveform 240 initially classified as an “artifact” may be reclassifiedas “abnormal. Then, features of one or both periodic components may beincluded with templates added to various databases.

FIG. 3 depicts an example method 300 that may be performed by one ormore components of cardiovascular analysis system 124, in accordancewith various embodiments. In some embodiments, method 300 may beperformed initially once a patient is hooked up to one or more healthmonitoring devices (e.g., 104-112 in FIG. 1) to generate various typesof templates (e.g., normal, various categories of abnormal) associatedwith the patient. In some embodiments, method 300 may cease beingperformed for the patient when a sufficient number of templates aregenerated. For example, once there are at least a threshold number oftemplates having a particular classification (e.g., normal, varioustypes of abnormal), method 300 may no longer be performed unless medicalpersonnel determine that the templates are flawed, in which case theymay activate a “relearn” routine which starts template generation forthe patient over. While operations of FIG. 3 are depicted in aparticular order, this is not meant to be limiting. In variousembodiments, one or more operations may reordered, omitted, or added.

At block 302, a classification assigned to a periodic component of anelectrical waveform (e.g., signal 114 produced by ECG 104) may beidentified. For example, analysis engine 126 may identify an annotationassigned by initial annotation engine 125 to a periodic component insignal 114. Or, analysis engine 126 may analyze a previously-recordedsignal waveform with annotations, which may or may not have been addedby initial annotation engine 125. At block 304, a corresponding periodiccomponent of a hemodynamic waveform from the same patient may beidentified. As mentioned above, the corresponding periodic component maybe causally related to the periodic component of the electricalwaveform, and in many cases may be identified as corresponding becauseit trails the electrical periodic component by some predictable timeinterval or range of time intervals.

At block 306, a signal quality index, or “SQI,” of the periodiccomponent of the hemodynamic waveform (or associated with the entirehemodynamic waveform or a portion thereof including multiple periodiccomponents) may be determined. The SQI may be calculated in variousways. In some embodiments, for instance, a SQI associated with an ABPsignal may estimate blood pressure at various phases in the cardiaccycle and assign scores based on the physiological plausibility of thesignal. Some SQIs may measure morphological normality. Others maymeasure degradation of the signal due to noise (or artifacts). If thesignal represented by the waveform is relatively noisy, that waveformmay receive a relatively low SQI. Other SQIs not specifically mentionedherein may be applied as well.

If the SQI determined at block 306 fails to satisfy some threshold T(which may be, for instance, set to various values), then method 300 mayproceed to block 308, at which point the corresponding periodiccomponent of the hemodynamic waveform that was originally identified atblock 304 may be rejected. However, if the SQI satisfies the thresholdti, then method may proceed to block 310.

At block 310, it may be determined whether the classification assignedto the periodic component of the electric waveform (which was identifiedat block 302) is “normal” or some equivalent variation thereof. If theanswer is yes, then method 300 may proceed to block 312. At block 312,the corresponding component of the hemodynamic waveform identified atblock 304 may be classified as “normal” (or the semantically equivalentvariation thereof).

Then, at block 314, one or more features extracted from thenow-classified corresponding periodic component may be incorporated intoa “normal” hemodynamic template stored in the hemodynamic templatedatabase 130. In some embodiments, incorporating the one or features mayinclude coalescing the now-classified corresponding periodic componentwith a already-coalesced periodic component stored in association withthe “normal” hemodynamic template. For example, time durations of thetwo periodic components may be normalized, and then an average of thetwo periodic components may be determined.

In some embodiments, a weighted average of the now-classified periodiccomponent with n periodic components represented by the template may bedetermined. For example, the periodic component of the “normal”hemodynamic template may include an average of the past n periodiccomponents added to the template, each which may or may not be weightedin accordance to how recently they were added. The further back in timea particular periodic component was incorporated into the template(e.g., determined in terms of iterations or pure time), the less weightit may be assigned. Put another way, exponential weights that decreasefurther in the past may be assigned to the n periodic components thatwere coalesced into the template. In some embodiments, a low pass filtersuch as a single pole filter may be employed to determine the periodiccomponent stored in association with the template.

Back at block 310, if the periodic component of the electrical signalwas not annotated as “normal,” then method 300 may proceed to block 316.At block 316, it may be determined whether the classification assignedto the periodic component of the electric waveform (which was identifiedat block 302) is “artifact, “noise,” or some equivalent variationthereof. If the answer is yes, then method 300 may proceed to block 318.At block 318, it may be determined whether the corresponding periodiccomponent of the hemodynamic waveform satisfies one or more criteria.For example, in some embodiments, it may be determined whether the Δmentioned previously—that is, the difference between amplitudes betweentwo adjacent peaks—satisfies some threshold. In some embodiments, anequation such as the following may be employed:

$\Delta = {\frac{{{SBP}\left( {n - 1} \right)} - {{SBP}(n)}}{{SBP}\left( {n - 1} \right)} > {5\%}}$

wherein SBP(n) is the systolic blood pressure at the periodic componentunder examination and SBP(n-1) is the systolic blood pressure at theimmediately preceding periodic component. While 5% is used as athreshold in this example, it should be understood that this is notmeant to be limiting. Various other thresholds may be selected undervarious circumstances, depending on, among other things, the health ofthe patient, the circumstances of the patient (e.g., activity in whichthey are engaged), and so forth.

If the criteria of block 318 are not satisfied, then method 300 mayproceed to block 320, at which point the corresponding periodiccomponent may be rejected. This may indicate, for instance, that theperiodic component of the electrical waveform was, in fact, properlyclassified as an artifact. On the other hand, if the criteria of block318 are satisfied, then method 300 may proceed to block 322. At block322, the periodic component of the electrical waveform may bereclassified, in this instance from “artifact” to “abnormal.” At block324, the corresponding periodic component of the hemodynamic waveformmay likewise be classified as “abnormal.” And as was already discussed,at block 314, one or features of the now-classified correspondingperiodic component of the hemodynamic waveform may be incorporated intoan “abnormal” template stored in the database (e.g., 130).

Back at block 316, if the periodic component of the electrical signalwas not annotated as “noise” or an “artifact,” then method 300 mayproceed to block 326. At block 326, it may be determined whether theclassification assigned to the periodic component of the electricwaveform (which was identified at block 302) is “abnormal” or someequivalent variation thereof. If the answer is no, then method 300 mayproceed back to block 302. However, if the answer at block 326 is yes,then method 300 may proceed to block 328. At block 328, a similardetermination may be made as was made at block 318. For example, thesame or similar equation as was used above may be used again. If thethreshold at block 328 is not satisfied, then method 300 may proceed toblock 320, at which point the periodic component of the hemodynamicwaveform may be rejected. If the threshold at block 328 is satisfied,then method 300 may proceed to block 324 and then block 314, which weredescribed above.

In FIG. 3, a single SQI determination is made at block 306, but this isnot meant to be limiting. In various embodiments, different SQIdeterminations may be made depending on the classification assigned tothe periodic component of the electrical waveform. For example, if it isclassified as normal, a first SQI may be determined. If it is classifiedas abnormal, a second SQI may be determined. And so on. And while theparticular equation described above may be used to determine abnormalityin a hemodynamic waveform, that is not meant to suggest that it can onlybe used in isolation, or that alternative equations cannot be used.

FIG. 4 depicts an example method 400 for using the templates developedusing methods such as that depicted in FIG. 3 to classify unclassifiedperiodic waveform components, as well as for continuing to addhemodynamic templates to one or more databases, such as 130. As was thecase with method 300, while operations of FIG. 4 are depicted in aparticular order, this is not meant to be limiting. In variousembodiments, one or more operations may reordered, omitted, or added.

At block 402, the hemodynamic waveform under consideration may besegmented, e.g., into segments that each includes a periodic componentsuch as a peak and/or a valley. At block 404, a SQI may be determinedfor each segment (or the waveform as a whole), similar to block 306 ofFIG. 3. If the SQI fails to satisfy a threshold ti, then method 400 mayproceed to block 406, at which point the segment (i.e. a portion of thewaveform containing a periodic component) may be classified as “noise”or an “artifact.” If the threshold is satisfied, however, then method400 may proceed to block 408.

At block 408, each segment/periodic component may be correlated totemplates in a template databases (e.g., 130). For example, in someembodiments, the segment/periodic component may be correlated to both anormal template and a plurality of abnormal templates, each associatedwith a different type of abnormal classification (e.g., premature beat,atrial fibrillation, etc.). In some embodiments, and as was mentionedpreviously, the segment/periodic component under analysis may besubtracted from the template periodic component(s), and the differencemay be compared to the one or more thresholds. In other embodiments,techniques such as FFTs or covariance shifting may be employed tocorrelate the periodic component under analysis to one or more templateperiodic components.

If at block 410 the correlation R between the segment/periodic componentand one or more templates fails to satisfy another threshold (τ₂ in FIG.4), then method 400 may proceed to block 412, at which point theperiodic component may be classified as inconclusive and/or rejected. Ifthe threshold τ₂ is satisfied, however, then method 400 may proceed toblock 414. In various embodiments, τ₂ may be an adjustable threshold forevaluating a correlation between the template the segment/periodiccomponent. In some embodiments, τ₂ may set to values between 0.5 and 1,such as 0.8. In other embodiments, τ₂ may be learned over time frompatient data (e.g., using machine learning techniques or variousheuristics). For example, as more templates are added to the templatedatabase, it may be possible to obtain closer matches, and hence τ₂ maychange over time.

At block 414, one or more features of the segment (i.e. periodiccomponent) may be incorporated into a template stored in a templatedatabases (e.g., 130). Examples of how periodic components may beincorporated into a template were described above with respect to block314. At block 416, the segment/periodic component may be classifiedaccordingly, e.g., for use by one or more downstream components and/oralgorithms.

Waveforms having their periodic componentsclassified/reclassified/annotated using techniques described herein maybe used for various downstream purposes. For example, a periodiccomponent of a hemodynamic waveform such as a peak that is classified asabnormal using techniques described herein may be used to detect andalert hemodynamic deterioration. Additionally, other cardiovascularmeasurements may be made more accurate when viewed in conjunction withannotated periodic components. For example, heart rate turbulence may bemore accurately identified based at least in part onclassifications/annotations determined using disclosed techniques. Insome embodiments, techniques described herein may be used with sleepmonitoring systems that simultaneously monitor ECG and PLETH signals.Additionally, hemodynamic waveforms annotated using techniques here maybe used for applications such as clinical decision support algorithms,e.g., to reduce false alarm rates by properly classifying periodiccomponents as abnormalities. As another example, techniques describedherein may be used to retroactively correct ECG artifacts in ECG signalsas abnormal.

FIG. 5 depicts non-limiting examples of how multiple accumulatedperiodic components (referred to in the image as “beats”) that have beenclassified as “normal” (top) and “premature” (bottom, i.e., a particulartype of abnormality) may be coalesced into a single cumulative periodiccomponent represented by the bold black line. If an unclassifiedperiodic component is sufficiently similar to those bold-black-lineperiodic components depicted in FIG. 5, it may be classifiedaccordingly. For example, if a similarity score between a feature vectorextracted from an unclassified periodic component and a feature vectorextracted from the coalesced cumulative periodic component shown in FIG.5 satisfies one or more thresholds, that unclassified periodic componentmay be classified the same.

FIG. 6 is a block diagram of an example computer system 610. Computersystem 610 typically includes at least one processor 614 whichcommunicates with a number of peripheral devices via bus subsystem 612.As used herein, the term “processor” will be understood to encompassvarious devices capable of performing the various functionalitiesattributed to the CDS system described herein such as, for example,microprocessors, FPGAs, ASICs, other similar devices, and combinationsthereof. These peripheral devices may include a data retention subsystem624, including, for example, a memory subsystem 625 and a file storagesubsystem 626, user interface output devices 620, user interface inputdevices 622, and a network interface subsystem 616. The input and outputdevices allow user interaction with computer system 610. Networkinterface subsystem 616 provides an interface to outside networks and iscoupled to corresponding interface devices in other computer systems.

User interface input devices 622 may include a keyboard, pointingdevices such as a mouse, trackball, touchpad, or graphics tablet, ascanner, a touchscreen incorporated into the display, audio inputdevices such as voice recognition systems, microphones, and/or othertypes of input devices. In general, use of the term “input device” isintended to include all possible types of devices and ways to inputinformation into computer system 610 or onto a communication network.

User interface output devices 620 may include a display subsystem, aprinter, a fax machine, or non-visual displays such as audio outputdevices. The display subsystem may include a cathode ray tube (CRT), aflat-panel device such as a liquid crystal display (LCD), a projectiondevice, or some other mechanism for creating a visible image. Thedisplay subsystem may also provide non-visual display such as via audiooutput devices. In general, use of the term “output device” is intendedto include all possible types of devices and ways to output informationfrom computer system 610 to the user or to another machine or computersystem.

Data retention system 624 stores programming and data constructs thatprovide the functionality of some or all of the modules describedherein. For example, the data retention system 624 may include the logicto perform selected aspects of methods 300 or 400, and/or to implementone or more components of cardiovascular analysis system 124.

These software modules are generally executed by processor 614 alone orin combination with other processors. Memory 625 used in the storagesubsystem can include a number of memories including a main randomaccess memory (RAM) 630 for storage of instructions and data duringprogram execution, a read only memory (ROM) 632 in which fixedinstructions are stored, and other types of memories such asinstruction/data caches (which may additionally or alternatively beintegral with at least one processor 614). A file storage subsystem 626can provide persistent storage for program and data files, and mayinclude a hard disk drive, a floppy disk drive along with associatedremovable media, a CD-ROM drive, an optical drive, or removable mediacartridges. The modules implementing the functionality of certainimplementations may be stored by file storage subsystem 626 in the dataretention system 624, or in other machines accessible by theprocessor(s) 614. As used herein, the term “non-transitorycomputer-readable medium” will be understood to encompass both volatilememory (e.g. DRAM and SRAM) and non-volatile memory (e.g. flash memory,magnetic storage, and optical storage) but to exclude transitorysignals.

Bus subsystem 612 provides a mechanism for letting the variouscomponents and subsystems of computer system 610 communicate with eachother as intended. Although bus subsystem 612 is shown schematically asa single bus, alternative implementations of the bus subsystem may usemultiple busses.

Computer system 610 can be of varying types including a workstation,server, computing cluster, blade server, server farm, or any other dataprocessing system or computing device. In some embodiments, computersystem 610 may be implemented within a cloud computing environment. Dueto the ever-changing nature of computers and networks, the descriptionof computer system 610 depicted in FIG. 6 is intended only as a specificexample for purposes of illustrating some implementations. Many otherconfigurations of computer system 610 are possible having more or fewercomponents than the computer system depicted in FIG. 6.

While several embodiments have been described and illustrated herein,those of ordinary skill in the art will readily envision a variety ofother means and/or structures for performing the function and/orobtaining the results and/or one or more of the advantages describedherein, and each of such variations and/or modifications is deemed to bewithin the scope of the embodiments described herein. More generally,those skilled in the art will readily appreciate that all parameters,dimensions, materials, and configurations described herein are meant tobe exemplary and that the actual parameters, dimensions, materials,and/or configurations will depend upon the specific application orapplications for which the teachings is/are used. Those skilled in theart will recognize, or be able to ascertain using no more than routineexperimentation, many equivalents to the specific embodiments describedherein. It is, therefore, to be understood that the foregoingembodiments are presented by way of example only and that, within thescope of the appended claims and equivalents thereto, embodiments may bepracticed otherwise than as specifically described and claimed.Inventive embodiments of the present disclosure are directed to eachindividual feature, system, article, material, kit, and/or methoddescribed herein. In addition, any combination of two or more suchfeatures, systems, articles, materials, kits, and/or methods, if suchfeatures, systems, articles, materials, kits, and/or methods are notmutually inconsistent, is included within the scope of the presentdisclosure.

All definitions, as defined and used herein, should be understood tocontrol over dictionary definitions, definitions in documentsincorporated by reference, and/or ordinary meanings of the definedterms.

The indefinite articles “a” and “an,” as used herein in thespecification and in the claims, unless clearly indicated to thecontrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, i.e., elements that are conjunctively present in some casesand disjunctively present in other cases. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined. Other elements may optionally be presentother than the elements specifically identified by the “and/or” clause,whether related or unrelated to those elements specifically identified.Thus, as a non-limiting example, a reference to “A and/or B”, when usedin conjunction with open-ended language such as “comprising” can refer,in one embodiment, to A only (optionally including elements other thanB); in another embodiment, to B only (optionally including elementsother than A); in yet another embodiment, to both A and B (optionallyincluding other elements); etc.

As used herein in the specification and in the claims, “or” should beunderstood to have the same meaning as “and/or” as defined above. Forexample, when separating items in a list, “or” or “and/or” shall beinterpreted as being inclusive, i.e., the inclusion of at least one, butalso including more than one, of a number or list of elements, and,optionally, additional unlisted items. Only terms clearly indicated tothe contrary, such as “only one of” or “exactly one of,” or, when usedin the claims, “consisting of,” will refer to the inclusion of exactlyone element of a number or list of elements. In general, the term “or”as used herein shall only be interpreted as indicating exclusivealternatives (i.e. “one or the other but not both”) when preceded byterms of exclusivity, such as “either,” “one of,” “only one of,” or“exactly one of.” “Consisting essentially of,” when used in the claims,shall have its ordinary meaning as used in the field of patent law.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, as anon-limiting example, “at least one of A and B” (or, equivalently, “atleast one of A or B,” or, equivalently “at least one of A and/or B”) canrefer, in one embodiment, to at least one, optionally including morethan one, A, with no B present (and optionally including elements otherthan B); in another embodiment, to at least one, optionally includingmore than one, B, with no A present (and optionally including elementsother than A); in yet another embodiment, to at least one, optionallyincluding more than one, A, and at least one, optionally including morethan one, B (and optionally including other elements); etc.

It should also be understood that, unless clearly indicated to thecontrary, in any methods claimed herein that include more than one stepor act, the order of the steps or acts of the method is not necessarilylimited to the order in which the steps or acts of the method arerecited.

In the claims, as well as in the specification above, all transitionalphrases such as “comprising,” “including,” “carrying,” “having,”“containing,” “involving,” “holding,” “composed of,” and the like are tobe understood to be open-ended, i.e., to mean including but not limitedto. Only the transitional phrases “consisting of” and “consistingessentially of” shall be closed or semi-closed transitional phrases,respectively, as set forth in the United States Patent Office Manual ofPatent Examining Procedures, Section 2111.03. It should be understoodthat certain expressions and reference signs used in the claims pursuantto Rule 6.2(b) of the Patent Cooperation Treaty (“PCT”) do not limit thescope

1. A computer-implemented method, comprising: identifying, by one ormore processors, a periodic component of an electrical waveform and apreviously-assigned classification assoctiated with the periodiccomponent of the electrical waveform, wherein the electrical waveformrepresents electrical activity in a patient's heart; analyzing, by oneor more of the processors, a corresponding periodic component of ahemodynamic waveform that represents hemodynamic activity in thepatient's cardiovascular system, wherein the corresponding periodiccomponent is causally related to the periodic component of theelectrical waveform; classifying, by one or more of the processors, thecorresponding periodic component of the hemodynamic waveform with thepreviously-assigned classification in response to a determination, basedon the analyzing, that the previously-assigned classification alsoapplies to the corresponding periodic component; and updating, by one ormore of the processors, in a database of hemodynamic templates, ahemodynamic template associated with the previously-assignedclassification to include one or more features of the correspondingperiodic component of the hemodynamic waveform.
 2. Thecomputer-implemented method of claim 1, further comprising receiving, byone or more of the processors, electrophysiological data associated withthe patient, wherein the electrophysiological data includes theelectrical waveform and one or more previously-assigned classificationsassociated with one or more periodic components of the electricalwaveform; and receiving, by one or more of the processors, hemodynamicdata associated with the patient, wherein the hemodynamic data includesthe hemodynamic waveform.
 3. The computer-implemented method of claim 2,wherein the electrophysiological data is received from one or moreelectrodes of an electrocardiogram.
 4. The computer-implemented methodof claim 2, wherein the hemodynamic data comprises a signal indicativeof at least one of arterial blood pressure of the patient, pulmonaryblood pressure of the patient, and central venous pressure. 5.(canceled)
 6. (canceled)
 7. The computer-implemented method of claim 2,wherein the hemodynamic data comprises a signal from a plethysmograph.8. The computer-implemented method of claim 1, further comprising:identifying, by one or more of the processors, an unclassified periodiccomponent of the same hemodynamic waveform or a different hemodynamicwaveform associated with a different patient; matching, by one or moreof the processors, the unclassified periodic component to a template ofthe database of hemodynamic templates; and classifying, by one or moreof the processors, the unclassified periodic component of thehemodynamic waveform with a classification associated with the matchingtemplate.
 9. The computer-implemented method of claim 8, furthercomprising updating, by one or more of the processors, the matchingtemplate to include one or more features of the now-classified periodiccomponent of the hemodynamic waveform.
 10. The computer-implementedmethod of claim 1, wherein the previously-assigned classificationcomprises an abnormal classification, and wherein the assigningcomprises assigning the abnormal classification to the correspondingperiodic component of the hemodynamic waveform in response to adetermination, based on the analysis, that a delta between thecorresponding periodic component of the hemodynamic waveform and aprevious periodic component of the hemodynamic waveform satisfies athreshold.
 11. The computer-implemented method of claim 1, furthercomprising: identifying, by one or more of the processors, an artifactclassification assigned to another periodic component of the electricalwaveform deemed an artifact; analyzing, by one or more of theprocessors, another corresponding periodic component of the hemodynamicwaveform that is causally related to the another periodic component ofthe electrical waveform; classifying, by one or more of the processors,the another corresponding periodic component of the hemodynamic waveformwith an abnormal classification in response to a determination, based onthe analyzing, that a delta between the another corresponding periodiccomponent of the hemodynamic waveform and another previous periodiccomponent of the hemodynamic waveform satisfies a threshold; andreclassifying, by one or more of the processors, the another periodiccomponent of the electrical waveform with the abnormal classification.12. The computer-implemented method of claim 1, wherein thepreviously-assigned classification comprises a normal classification,and wherein the assigning comprises assigning the normal classificationto the corresponding periodic component of the hemodynamic waveform inresponse to a determination, based on the analyzing, that thecorresponding periodic component satisfies a signal quality index(“SQI”).
 13. The computer-implemented method of claim 1, wherein theanalyzing comprises matching the corresponding periodic component to atemplate of the database of hemodynamic templates.
 14. Thecomputer-implemented method of claim 13, wherein the updating comprisescoalescing the corresponding periodic component with a periodiccomponent stored in association with the matching hemodynamic template.15. A system comprising: one or more processors; and memory operablycoupled with the one or more processors, wherein the memory stores adatabase of hemodynamic templates, wherein the database is constructedby the method from claim 1, wherein each hemodynamic template comprisesone or more features of one or more periodic components of a hemodynamicwaveform and a corresponding classification, wherein the hemodynamicwaveform represents hemodynamic activity in a patient's cardiovascularsystem, and wherein the memory further stores instructions that, inresponse to execution of the instructions by the one or more processors,cause the one or more processors to: identify an unclassified periodiccomponent of the hemodynamic waveform; match the unclassified periodiccomponent to a template of the database of hemodynamic templates;classify the unclassified periodic component of the hemodynamic waveformwith a classification associated with the matching template; and updatethe matching template to include one or more features of thenow-classified periodic component of the hemodynamic waveform.
 16. Atleast one non-transitory computer-readable medium comprisinginstructions that, in response to execution of the instructions by oneor more processors, cause the one or more processors to perform thefollowing operations: identifying a previously-assigned classificationassigned to a periodic component of an electrical waveform, wherein theelectrical waveform represents electrical activity in a patient's heart;analyzing a corresponding periodic component of a hemodynamic waveformthat represents hemodynamic activity in the patient's cardiovascularsystem, wherein the corresponding periodic component is causally relatedto the periodic component of the electrical waveform; classifying thecorresponding periodic component of the hemodynamic waveform with thepreviously-assigned classification in response to a determination, basedon the analyzing, that the previously-assigned classification alsoapplies to the corresponding periodic component; and updating, in adatabase of hemodynamic templates, a hemodynamic template associatedwith the previously-assigned classification to include one or morefeatures of the corresponding periodic component of the hemodynamicwaveform.
 17. The at least one non-transitory computer-readable mediumof claim 16, further comprising instructions for receivingelectrophysiological data associated with the patient, wherein theelectrophysiological data includes the electrical waveform and one ormore classifications associated with one or more periodic components ofthe electrical waveform; and receiving hemodynamic data associated withthe patient, wherein the hemodynamic data includes the hemodynamicwaveform.
 18. The at least one non-transitory computer-readable mediumof claim 17, wherein the electrophysiological data is received from oneor more electrodes of an electrocardiogram.
 19. The at least onenon-transitory computer-readable medium of claim 17, wherein thehemodynamic data comprises a signal indicative of arterial bloodpressure of the patient.
 20. The at least one non-transitorycomputer-readable medium of claim 17, wherein the hemodynamic datacomprises a signal indicative of pulmonary blood pressure of thepatient.
 21. The computer-implemented method of claim 1, wherein thepreviously-assigned classification comprises one of a normalclassification, an artifact classification, and an abnormalclassification.
 22. A system comprising: one or more processors; andmemory operably coupled with the one or more processors, wherein thememory stores a database of hemodynamic templates, and wherein thememory further stores instructions that, in response to execution of theinstructions by the one or more processors, cause the one or moreprocessors to: identify a periodic component of an electrical waveformand a previously-assigned classification associated with the periodiccomponent of the electrical waveform, wherein the electrical waveformrepresents electrical activity in a patient's heart; analyze acorresponding periodic component of a hemodynamic waveform thatrepresents hemodynamic activity in the patient's cardiovascular system,wherein the corresponding periodic component is causally related to theperiodic component of the electrical waveform; classify thecorresponding periodic component of the hemodynamic waveform with thepreviously-assigned classification in response to a determination, basedon the analyzing, that the previously-assigned classification alsoapplies to the corresponding periodic component; and update in thedatabase a hemodynamic template associated with the previously assignedclassification to include one or more features of the correspondingperiodic component of the hemodynamic waveform.